AIMC Topic: Pollen

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Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

The Science of the total environment Jul 8, 2021
Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, p...
Humans Deep Learning Allergens Pollen Rhinitis, Allergic, Seasonal Environmental Monitoring Seasons
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Neural networks for increased accuracy of allergenic pollen monitoring.

Scientific reports May 31, 2021
Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in imag...
Pollen Parietaria Neural Networks, Computer Seasons Allergens Environmental Monitoring
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Deep Learning Methods for Improving Pollen Monitoring.

Sensors (Basel, Switzerland) May 19, 2021
The risk of pollen-induced allergies can be determined and predicted based on data derived from pollen monitoring. Hirst-type samplers are sensors that allow airborne pollen grains to be detected and their number to be determined. Airborne pollen gra...
Rhinitis, Allergic, Seasonal Deep Learning Pollen Humans Neural Networks, Computer
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Deep learning in deep time.

Proceedings of the National Academy of Sciences of the United States of America Nov 9, 2020
Microscopy Pollen Fossils Neural Networks, Computer Deep Learning
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Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.

Proceedings of the National Academy of Sciences of the United States of America Oct 23, 2020
Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution ...
Pollen Fossils Phylogeography Africa, Western Machine Learning Neural Networks, Computer Phylogeny Microscopy South America Africa
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Pollen analysis using multispectral imaging flow cytometry and deep learning.

The New phytologist Sep 22, 2020
Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensi...
Pollen Flow Cytometry Pollination Phylogeny Deep Learning
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Precise automatic classification of 46 different pollen types with convolutional neural networks.

PloS one Jun 23, 2020
In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual ...
Computational Biology Deep Learning Automation Pollen Databases, Factual Image Processing, Computer-Assisted
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DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana.

The Plant journal : for cell and molecular biology Oct 22, 2019
Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be mea...
Arabidopsis Pollen Crossing Over, Genetic Chromosome Segregation Deep Learning Homologous Recombination Meiosis Chromosomes, Plant Alleles Arabidopsis Proteins Transgenes
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Precise Pollen Grain Detection in Bright Field Microscopy Using Deep Learning Techniques.

Sensors (Basel, Switzerland) Aug 17, 2019
The determination of daily concentrations of atmospheric pollen is important in the medical and biological fields. Obtaining pollen concentrations is a complex and time-consuming task for specialized personnel. The automatic location of pollen grains...
Reproducibility of Results Pollen Deep Learning Videotape Recording Microscopy
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Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Environmental monitoring and assessment Jun 7, 2019
Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen de...
Oklahoma Weather Radar Plant Extracts Allergens Pollen Environmental Monitoring Antigens, Plant Atmosphere Machine Learning Forecasting
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